Generative AI: Elevate your Software Development Career

IBM via Coursera

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Overview

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Generative AI (GenAI) is now an essential skill for software developers keen to future-proof their careers. This course gives existing and aspiring developers the job-ready skills employers are looking for to build intelligent, adaptive, and secure solutions. You'll learn how to harness generative AI for code generation, software design, bug detection, testing, optimization, and documentation. You'll also explore how AI integrates into DevSecOps and continuous integration and continuous deployment (CI/CD) pipelines, supports translation, review, and security, and strengthens software delivery. You’ll examine large language models (LLMs), natural language processing (NLP), and prompt engineering to gain insight into how to use these technologies effectively. Plus, you’ll look at agentic AI and retrieval-augmented generation (RAG) for extending autonomy, and you’ll get hands-on with vibe coding tools and practices that transform how developers interact with code. You’ll also consider ethical and responsible AI practices to ensure you innovate responsibly. If you’re keen to leverage GenAI to build software faster and better so you can power your career forward, enroll today!

Syllabus

Generative AI Basics for Software Development

In this module, you'll explore how modern LLMs and prompt design accelerate everyday coding tasks. You'll learn how to set up your AI environment, craft effective prompts, and evaluate outputs for accuracy and safety. You'll gain hands-on experience generating small code units, refactoring legacy snippets, and producing quick diagrams or static site prototypes—then you'll verify, test, and iterate your prototype to improve results.

Generative AI for Software Architecture and Code Generation

In this module, you will explore how generative AI supports software architecture, design patterns, and code generation. You will use AI tools to generate architecture and design diagrams, create database schemas from requirements, build web prototypes, and troubleshoot coding problems. The module emphasizes best practices for scalable, maintainable, and efficient software design using AI assistance.

Generative AI for Software Development Workflows and Its Considerations

In this module, you will learn how generative AI transforms software development by integrating advanced AI techniques into continuous integration and deployment (CI/CD), security, testing, and documentation workflows. You will gain hands-on experience with practical tools like Docker and AI-powered test case generation, code translation, and review. Additionally, you will explore essential considerations, including ethical use, security, and best practices for embedding AI in development lifecycles. To support your learning, the module also provides a concise cheat sheet summarizing key concepts and tools related to generative AI in DevSecOps and software testing, serving as a quick reference to enhance your workflow.

Agentic AI and Vibe Coding

This module introduces you to the fundamentals of agentic AI and vibe coding. Learn what AI agents are, how they are built using frameworks like N8N and RAG, and how to integrate AI agents with your data and APIs. You'll also explore AI-assisted coding approaches, compare these approaches with traditional methods, and experiment with tools such as Cursor and Bolt. By the end of this module, you'll be able to demonstrate your ability to apply core concepts and practices that make modern coding more adaptive and intelligent.

Final Project

In this final module, you will consolidate your learning by engaging with a comprehensive project that challenges you to build a personalized learning platform for developers using generative AI. Starting with pre-work to organize your approach, you will explore the project scope and objectives in detail before applying generative AI models to create adaptive learning experiences in CodeCraftHub. The module culminates with a final exam that assesses your understanding and ability to apply generative AI concepts effectively in software development scenarios, preparing you for real-world implementation.